Geographically Distributed Data Centers Research Articles

Flexibility from networks of data centers: A market clearing formulation with virtual links

Data centers owned and operated by large companies have a high power consumption that is expected to increase in the future. However, the ability to shift computing loads geographically and in time can provide flexibility to the power grid. We introduce the concept of virtual links to capture space-time load flexibility provided by geographically-distributed data centers in market clearing procedures. We show that the virtual link abstraction fits well into existing market clearing frameworks and can help analyze and establish market design properties. This is demonstrated using illustrative case studies.

Autonomous Network and IT Resource Management for Geographically Distributed Data Centers

As enterprises struggle to meet the demand for elastic cloud computing services in distributed data centers (DCs), a significant portion of the total expenditure is spent on the procurement and maintenance of server and network equipment. Network and server resource utilization efficiency is therefore crucial to minimize overall costs and increase return on investment. We present an adaptive and synergistic management of both network and IT resources with an autonomous control plane on a converged inter/ intra-DC network to address this issue. The control plane uses its awareness of both network and IT resource usage to provide dynamic resource allocations to meet quality of service guarantees, while also allowing for reduction in operational expenses by consolidating virtual machines to maintain high CPU usage on the active servers. We built a three-node inter-DC experimental testbed to demonstrate the feasibility of our concept and show that our algorithm successfully provisions both network resources and consolidates IT resources under various load conditions.

Pervasive Cloud Controller for Geotemporal Inputs

The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures. Furthermore, to account for emerging technologies surrounding the cloud ecosystem, a maintainable control solution needs to be forward-compatible. Existing cloud controllers are focused on VM consolidation methods suitable only for a single data center or consider migration just in case of workload peaks, not accounting for all the aspects of geographically distributed data centers. In this paper, we propose a pervasive cloud controller for dynamic resource reallocation adapting to volatile time- and location-dependent factors, while considering the QoS impact of too frequent migrations and the data quality limits of time series forecasting methods. The controller is designed with extensible decision support components. We evaluate it in a simulation using historical traces of electricity prices and temperatures. By optimising for these additional factors, we estimate 28.6% energy cost savings compared to baseline dynamic VM consolidation. We provide a range of guidelines for cloud providers, showing the environment conditions necessary to achieve significant cost savings and we validate the controller's extensibility.

On the tradeoff of availability and consistency for quorum systems in data center networks

Large-scale distributed storage systems often replicate data across servers and even geographically-distributed data centers for high availability, while existing theories like CAP and PACELC show that there is a tradeoff between availability and consistency. Thus eventual consistency is proposed to provide highly available storage systems. However, current practice is mainly experience-based and lacks quantitative analysis for identifying a good tradeoff between the two. In this work, we are concerned with providing a quantitative analysis on availability for widely-used quorum systems in data center networks. First, a probabilistic model is proposed to quantify availability for typical data center networks: 2-tier basic tree, 3-tier basic tree, fat tree and folded clos, and even geo-distributed data center networks. Second, we analyze replica placements on network topologies to obtain maximal availability. Third, we build the availability-consistency table and propose a set of rules to quantitatively make tradeoff between availability and consistency. Finally, with Monte Carlo based simulations, we validate our presented quantitative results and show that our approach to make tradeoff between availability and consistency is effective.

Thermal-Aware Scheduling of Batch Jobs in Geographically Distributed Data Centers

Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm - GreFar - which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to T-unaware.